Abstract
With much more higher requirement for the precision of flood forecasting and length of forecast period in the hydrological operational predication, three coupling forecast methods which include real-time correction—combination forecast (RC-CF) method, combination forecast—real-time correction (CF-RC) method, and Integral Parameters Optimization (IPO) method are proposed in this paper for the purpose of improving the precision of flood forecasting. These coupling forecast methods are based on the real-time correction and combination forecast methods. Thereafter, two methods (method A & method B) are proposed for the purpose of prolonging the forecast period. Furthermore, indices of accuracy assessment which consist of mean absolute error, mean relative error, certainty coefficient and root-mean-square error are utilized to evaluate the forecast results of coupling forecast methods. Moreover, with a case study of Xiangjiaba station in the Jinsha River, advantages and disadvantages of these coupling forecast methods are obtained through the comparison of forecast results calculated by these methods, and they provide the basis for selection of coupling forecast methods. The result shows that the IPO method performs better than other two methods which behave undifferentiated. It is found that the IPO method combined with method B can be a viable alternative for flood forecasting of multiple hydrological models.
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Acknowledgments
This work is funded by the National Natural Science Foundation Key Project of China (51239004). Special thanks are given to the anonymous reviewers and editors for their constructive comments.
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Highlights of this Paper
(1) Real-time error correction combined with multi-model combination forecast
(2) Coupling forecast methods of multiple rainfall-runoff models
(3) Obvious forecast precision improvement and forecast period prolonging
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Wu, J., Zhou, J., Chen, L. et al. Coupling Forecast Methods of Multiple Rainfall–Runoff Models for Improving the Precision of Hydrological Forecasting. Water Resour Manage 29, 5091–5108 (2015). https://doi.org/10.1007/s11269-015-1106-8
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DOI: https://doi.org/10.1007/s11269-015-1106-8